An Overview of AI-Powered Handwriting Recognition Progress, Challenges, and Applications

This study explores the progress in optical character recognition (OCR) and handwriting recognition systems, focusing on the transformative impact of artificial intelligence (AI) in enhancing accuracy, precision, and scalability. These methods have been tried out for a number of uses, including multilingual text analysis, signature verification (SV), and handwritten text recognition (HTR). Many datasets have shown amazing achievements in online handwritten word and digit recognition. Transformer architecture and CNN-RNN hybrids are high-end models that have been shown to work well at capturing spatial and temporal data and adapting to differences in handwriting in different writing and language environments. The research stresses the need to analyze textural and structural features for improving AI-based handwriting systems while also talking about how important it is to get fine-grained features like pen pressure and stroke dynamics. Even with recent progress, there is still the challenge of dealing with different types of handwriting, changing styles, and scripts that aren't well represented, including non-Latin languages. Some study subjects that haven't been looked into yet are making systems more robust, changing models to better deal with uncertainty, and looking at how they might be used in forensic investigation, teaching, and medical diagnostics.

کلیدواژه ها:

OCR ، Handwriting Recognition ، AI ، Signature Verification ، CNN